System and method for predicting consumer credit risk using income risk based credit score

ABSTRACT

Systems and methods are described for scoring consumers&#39; credit risk by determining consumers&#39; income risk and future ability to pay. Methods are provided for measuring consumers&#39; income risk by analyzing consumers&#39; income loss risk, income reduction risk, probability of continuance of income, and economy&#39;s impact on consumers&#39; income. In one embodiment, a method is provided to evaluate an individual&#39;s creditworthiness using income risk based credit score thereby providing creditors, lenders, marketers, and companies with deeper, new insights into consumer&#39;s credit risk and repayment potential. By predicting consumers&#39; income risk and the associated creditworthiness the present invention increases the accuracy and reliability of consumers&#39; credit risk assessments, results in more predictive and precise consumer credit scoring, and offers a new method of rendering a forward-looking appraisal of an individual&#39;s ability to repay a debt or the ability to pay for products and services.

RELATED APPLICATION

This claims the benefit of U.S. Provisional Application No. 61/247,421,filed Sep. 30, 2009, the entire contents of which are incorporatedherein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to the field of consumer creditscoring and credit risk prediction, and, more particularly, the presentinvention relates to the utilization of a novel income risk based creditscoring system using an individual's unemployment risk probability andincome loss risk, and factoring the impact of economy on consumers'credit risk, to increase the accuracy of consumer credit risk forecastsresulting in credit loss reductions, increase in acquisitions, increasein portfolio credit quality, and an increase in profitability in theconsumer credit industry.

2. Description of the Background

Individual borrowers pay their loans or loan installments when they havethe ability to pay. The ability to pay largely depends on a person'sdisposable income. And if a person's disposable income disappears due tothe loss of his job, or due to income reduction resulting from a pay cutor a change in job or due to underemployment, then the person assumes amuch higher risk of defaulting on his loan repayments simply because theperson has no money and therefore has no ability to pay. That is why itis critical to predict a person's ability to pay based on his futureprobability of loss of income or a reduction in income in order to makea superior prediction of his creditworthiness. Today, the standardapproach to credit scoring is through traditional credit scores but theproblem is that they are increasingly becoming inaccurate, simplybecause they don't predict future ability to pay. They are essentiallyreactive scores, meaning they change after borrowers default, and do notfactor changes in the economy, and purely rely on credit histories andconsumers' past ability to pay.

The problem this invention solves is that traditional credit bureauscores are not very accurate and have many significant limitations.Specifically, there are 3 problems with credit bureau scores. Firstproblem is that credit bureau scores are reactive scores. The reasoncredit bureau scores are reactive is because they change only after theborrower defaults. The second problem is that credit bureau does notconsider borrowers' income risk and that is why they can never be veryaccurate in predicting credit risk. The third problem with credit bureauscores is that they cannot score about 70 m people. This is becausecredit bureau scores can only be generated for people, who have longcredit histories, but some 70 million people do not have credithistories or have very limited credit histories, and hence credit bureaumodel cannot score them. This means most lenders are not able to dobusiness with these 70 million people.

To appreciate credit bureau score limitations let's take a look atcredit bureau factors. The five key factors and their contribution tothe overall credit bureau score are: payment history (35%), amount owed(30%), length of credit history (15%), types of credit (10%), and newcredit (10%). As can be seen, credit bureau scores are entirely based onpast credit behavior and does not factor future income risk or impact ofeconomy on consumer's ability to pay. So, essentially credit bureauscore is a measure of past credit risk and would work only for thosepeople whose risk profile and income risk has not changed or beenaffected because of changes in the economy and business conditions.

A person is only able to repay a loan if the monetary sources areavailable which is usually dependent on consumer's continuance ofpresent income and on consumer's intent to pay; thus, in effect theconsumer's total credit is a function of both the willingness to pay andthe “ability to pay.” Since an individual's ability to pay is directlyrelated to continuance of income, defining that individual's credit riskusing income loss risk and unemployment probability greatly increasesthe accuracy and effectiveness of credit risk prediction. Although, theconsumer's income risk is a critical driver of credit risk it is not afactor in existing credit bureau scores.

The ability to pay is a critical factor in predicting credit risk,because a borrower must have both the willingness and the ability torepay a loan. If any one factor is missing then lenders will not gettheir payment. So the bottom line is that credit risk equals willingnessto pay plus the ability to pay. And while it is useful to know the pastwillingness and ability, what really matters is the future willingnessand future ability. And the future ability to pay depends on theborrower as well as the economic conditions, just as ‘accident risk’depends on both the ‘driver’ and the ‘driving conditions’. Since abilityto pay is such a critical driver of consumer creditworthiness,considering consumers' income risk and ability to pay in addition to thecredit histories and payment histories will greatly enhance thepredictive power of credit scoring models.

Consumer credit has traditionally been regarded to have threecomponents: Collateral, Capacity, and Character (or Willingness).However, there is no collateral in cases of unsecured loans such ascredit cards, capacity is equated with current income level, andwillingness is judged based on past payment behavior. While creditbureau scores are based on the idea that a borrower's past paymentbehavior is indicative of their future payment behavior, a person'sprevious ability to pay is a less reliable predictor of future abilityto pay compared to future continuance of income. Therefore existingcredit scoring models fail to take into account consumer's true“capacity” to pay or ability to pay which depends on consumer's futurecontinuance or income risk. But the present invention addresses thisunmet need by providing a method to determine a consumer's income riskand the dependent credit risk.

As of September 2009, the Applicant is the only provider of income riskbased credit score in the industry. No other invention has been able toso accurately calculate an unemployment probability and ability to payand, more importantly, incorporate income risk into a credit scoringsystem to offer new, better credit risk insights resulting in effectiveand accurate consumer credit risk predictions.

One embodiment of the income risk based credit score is the Job SecurityScore which is generated by a novel credit scoring system complaint withthe Equal Credit Opportunity Act's (ECOA) Regulation B. As defined inRegulation B, a “credit scoring system” is a system that evaluates anapplicant's creditworthiness mechanically, based on key attributes ofthe applicant and aspects of the transaction. It determines, alone or inconjunction with an evaluation of additional information about theapplicant, whether an applicant is deemed creditworthy. 12 C.F.R.§202.2(p)(1).

Also, the Job Security Score qualifies as “an empirically derived,demonstrably and statistically sound, credit scoring system” as definedby Reg B. The Regulation B states:

-   -   To qualify as an empirically derived, demonstrably and        statistically sound, credit scoring system, the system must be—        -   i. based on data that are derived from an empirical            comparison of sample groups or the population of            creditworthy and noncreditworthy applicants who applied for            credit within a reasonable period of time;        -   ii. developed for the purpose of evaluating the            creditworthiness of applicants with respect to the            legitimate business interests of the creditor utilizing the            system (including, but not limited to, minimizing bad debt            losses and operating expenses in accordance with the            creditor's business judgment);        -   iii. developed and validated using accepted statistical            principles and methodology; and        -   iv. periodically revalidated by use of appropriate            statistical principles and methodology and adjusted as            necessary to maintain predictive ability.            Id. The regulation goes on to state:    -   A creditor may use an empirically derived, demonstrably and        statistically sound, credit scoring system obtained from another        person or obtain credit experience from which to develop such a        system. Any such system must satisfy the criteria set forth in        paragraph (p)(1)(i) through (iv) of this section; if the        creditor is unable during the development process to validate        the system based on its own credit experience in accordance with        paragraph (p)(1) of this section, the system must be validated        when sufficient credit experience becomes available.

The current system predicts consumer creditworthiness by predicting anindividual's income risk and by empirical comparison of income risk andcredit experiences of a large population of creditworthy andnon-creditworthy applicants or accounts. The key difference betweentraditional credit scores and current invention is that traditionalcredit scoring systems compare an applicant's credit profile to creditexperiences of others whereas the current scoring system compares anapplicant's income risk profile to credit experiences of others.Consumers who have more stable income outlook because they have more jobsecurity are likely to be more creditworthy, which is proven by the factthat unemployed individuals default on their payment obligations a lotmore than employed individuals. The current invention uses an innovativeapproach of using consumers' income risk in predicting their credit riskand has created a credit scoring system through empirical comparison andanalysis of income loss experiences and credit default experiences.

Current bureau scoring models only take into account previous consumercredit transactions when creating a credit score and do not attempt tofactor a key driver of credit risk which is lack of sufficient income.Current credit bureau scoring models predominantly use payment history,amounts owed on account, length of credit history, new credit inquiries,and types of credit used, and do not use probability of incomecontinuance. They have not yet developed a forecasting method capable ofgenerating future income predictions of consumers, and therefore, haveno way to analyze a consumer's ability to pay. In addition, existingcredit scoring models are unable to score consumers with little-to-nocredit history, leaving a wide gap in its current scoring capabilities.

Other companies have attempted to supplement the credit scoring bureaus,but none have succeeded to the level of the current invention. This isdue to the fact that all are based on credit data and payment data. Noneinclude a forecast of future income risk or unemployment probability asa factor in consumer credit risk assessment. Thus, they are restrictedin their ability to make accurate credit risk predictions.

The current invention is a novel income loss based credit scoring modelthat is different from all known credit scoring models, and wasconstructed based on the personal data, employment and unemploymenthistories, and financial stress experiences of individuals from anational sample between hundred thousand and one million people andcredit behavior data from actual borrowers numbering between one millionand fifteen million borrowers. It takes into account the impact of thechanging economy on consumers' income risk and the dependent credit riskby considering: national and local macroeconomic attributes such as thegross domestic product, unemployment rates, retail sales, inflation,bankruptcies, foreclosures, money supply, and energy prices; andattributes that pertain to a group of individuals, such as type ofemployer and occupation; data for individuals, such as income, years atpresent job, and years at present residence; and by finding patterns andmathematical relationships between historical macroeconomic data andeconomic conditions and individuals and their historical income risk,ability to pay, and credit risk. The model uses various modelingtechniques to predict the likelihood of unemployment and credit risk upto thirty-six months in advance. The income risk based credit score canbe used alone or in conjunction with other scoring models, e.g. FICO,for functions such as deciding whether to grant or deny a credit,setting credit limits, or reviewing the performance of an existingaccount.

Traditional credit scores, such as FICO scores, are generated entirelyfrom the credit bureau's files, but Job Security Score primarily usesconsumer's loan application data to generate income loss risk and thento make a prediction of consumer's creditworthiness. Since, the incomerisk based credit score does not rely on credit histories it can scoreeveryone including those consumers who have limited or no establishedcredit histories. Currently in the U.S. there are 40 to 70 millionconsumers who do not have any credit histories or have very littlecredit histories which means that traditional credit bureau scorescannot be meaningfully computed for them. However, the income risk basedcredit score and one of its embodiments, the Job Security Score, iseasily able to score all these consumers. This allows lenders to offercredit to “thin-file” and “no-file” applicants. For consumers withsufficiently long credit histories and meaningful credit bureau scores,the income risk based credit score can still be used in combination withFICO or credit scores to add new risk insights and to improve theaccuracy and effectiveness of consumer payment default evaluations.

The total yearly consumer credit card losses in the U.S. amount to over80 Billion dollars. Thus, there is a great need for more accuracy inconsumer credit risk prediction. One embodiment of the income risk basedcredit score, the Job Security Score, improves risk prediction by up to30%, where even a 5% reduction in credit losses will save the creditcard industry $4.1 billion annually (See FIG. 9). The increased abilityof lenders, businesses, and others to forecast the consumer's ability topay and credit default risk will enhance profitability by reducinglosses, improving acquisitions and marketing, and by earlyidentification of high default risk consumers.

SUMMARY OF THE INVENTION

Every year, millions of consumers face financial hardships due to incomedisruption events such as unemployment, income loss, and incomereduction. And a majority of these financially stressed consumersdefault on their payment obligations related to credit card loans, autoloans, mortgage loans, student loans, and other personal loans; fallbehind on various kinds of insurance premium payments including lifeinsurance, medical insurance, auto insurance and home insurance; andalso are unable to pay their rental, medical, utilities payments andother purchases, because the economy and business conditions haveimpacted their income continuity or has caused loss of income leavingthem with diminished ability to pay and transforming them into highcredit risk consumers. Therefore income risk, or income disruption risk,is a very important component and driver of consumer credit risk.

While income risk drives consumers' ability to pay, which in turnaffects consumers' creditworthiness, none of the existing credit scoringmodels use income risk to predict credit risk, and hence they areincomplete and inaccurate, and this has been clearly proved in thecurrent recession where traditional credit scoring models have failedand credit losses have doubled or tripled over expected loss ratessimply because traditional credit scoring models and conventional creditbureau scores have not been able to quantify income risk in a poor andvolatile economy, and millions of consumers with high credit scores havedefaulted because they experienced an income disruption event whichadversely impacted their ability to pay and decreased theircreditworthiness.

Hence, there is a great need for more sophisticated consumer credit riskassessment model that considers consumers' income risk. And thereforethe present invention of income risk based credit score is not only anovel method of predicting credit risk arising out of income risk but itis also solves a major problem faced by the credit scoring and thelending industry of making better, complete credit risk predictions andminimizing credit losses.

Using the income risk based credit score as a primary decision score orin combination with traditional credit scoring models is much needed bythe credit industry today than ever before because tens of millions ofjobs are lost every year (over 20 million jobs were lost in 2008),primarily due to economic and business conditions. And, about two-thirdsof all unemployed face some financial difficulties and develop anincreased credit default risk, and a much higher percentage ofunemployed actually default on their debt obligations compared to theemployed. In fact, job loss is the number one reason for credit andmortgage default. With the use of income risk based credit score,businesses gain the ability to understand customers' risk of becomingunemployed and defaulting on payments, allowing them to better targettheir products and services to consumers that best fit with their risktolerance and strategic goals.

One preferred embodiment of the current invention is the ability togenerate consumer specific unemployment probabilities and income riskprobabilities by collecting consumers' personal profile data includingemployment data, unemployment data, financial stress history; economicdata; and consumers' credit default data. This preferred embodimentsolves the problem of identifying, segmenting, and targeting consumerswith desired level of income risk in order to create better prospectscores for increasing response rates and improving marketingefficiencies.

Another significant limitation that the Applicant's invention overcomesis that the current scoring models are reactive, and not trulypredictive, since they change or react after the consumer demonstratesgood or bad credit behavior, so in effect existing models do not predictcredit behavior but merely reflect them and hence are lagging indicatorsof consumer credit risk. Essentially, credit bureau scores areretrospective and can generally have up to 12 month lag time incapturing an increase or decrease in consumer's default risk simplybecause they change only after the consumer defaults on payments orshows some negative credit behavior, or when the consumer demonstratesgood payment behavior over a long period of time, and it the wholeprocess of collecting, processing, updating bureau databases, refreshingscores on a periodic process, and sending them to lenders can betedious, error-prone and time consuming; and hence such lagging anddelayed credit risk insights could only be of limited use for lenders.In effect traditional credit bureau scores tend to make straight lineprojections of consumers credit risk, that is, good consumers willremain good and bad consumers will remain bad in the near term. However,that is not true for many consumers and good consumers can go badquickly and bad consumers can become good consumers in a very shorttime, and such consumers will not be properly identified and scored bytraditional scoring models because of their design limitations.

Another significant limitation that the Applicant's invention overcomesis that the current scoring models are unable to score approximately 40to 70 million “thin-file” and “no-hit” credit card portfolios. Thisinability to score 70 million consumers due to a lack of sufficientinformation is a fundamental flaw of the current credit scoring systems.Hence, a more comprehensive and accurate credit model that not onlyconsiders past willingness and past ability to pay, but also takes intoaccount the future ability to pay is greatly needed and provided by theincome risk based credit score. Applicant's invention does not requireconsumers' credit histories and can score any individual therebyproviding a 100% scoring coverage.

The income risk based credit score offers many innovative andsignificant improvements over traditional credit scores because: it doesnot rely histories which may not be accurate and current sincecollection of credit transaction data is a tedious and error proneprocess which is evidenced by the fact that a majority of credit bureaureports have errors and all credit bureaus have different data on thesame individual and that in most instances they come up with differentcredit scores for the same individual; it considers the impact of theeconomy on consumer's future income and predicts income risk which isnot a factor in current credit scoring models; it is updated monthlyusing the latest economic data since economy impacts consumers' futureincome; and it can score every individual irrespective of their credithistories.

The income risk based credit scoring model's databases are updatedmonthly by using as updated assessment of economic conditions and howthe new conditions are going to impact consumers future income or incomerisk, allowing the most current information to be used by lenders,businesses, and others. Thus, instead of waiting for negative items toappear on a consumer's credit report, the income risk based credit scorequantifies the source of credit risk, and that is the interactionbetween the economy and consumer's income prospects, enabling lenders toget an accurate assessment of consumer's potential for defaulting on apayment.

In another preferred embodiment of the income risk based credit score,the Job Security Score is usable as a prospect score which predictsresponse rates and improves acquisitions by allowing lenders andbusinesses to identify better prospects who are more likely to respondto a marketing offer and become better consumers. When the Job SecurityScore is combined with other scores, the quality of predictions increaseand therefore businesses can better target their products and servicesto consumers that fit best with their ability to pay.

In alternative embodiments, the present invention of income risk modelmay also involve the use of input variables such as age; personalincome; total debt; debt ratio (debt/available debt); number of timesdelinquent in last two years; savings account information (if oneexist); residency (city, state, and zip code); years at currentresidence; own/rent status; total yearly income; highest level ofeducation; education discipline/concentration; year attained;educational institution; years of full time work experience; currentemployer; length of time with present employer; self-employment (ifany); part-time/full-time status; work city, state and zip code; joboccupation area; employer's industry (name, SIC code); and totalemployees at place of work.

In short, Applicant provides a comprehensive consumer future behaviorprediction model that employs a number of novel methods to accuratelyforecast and implement the ability to pay component in consumer creditrisk scoring models and to increase accuracy of prospect scoring modelsby predicting income risk of consumers. The income risk based creditscore alone or in combination with credit bureau scores, e.g. FICO, andprospects scores will lead to an enhanced power to discriminate andsegment consumers, improving profitability for businesses. By using theincome risk based credit score, the total dollar losses due to greateridentification of potential charge-off/bankrupt accounts will decrease,the good accounts volume will increase, and the portfolio performancewill improve. By using the income risk based credit score, decisions ondecreasing the credit line for these accounts can be made before theseaccounts become problematic and loss prone. Similarly, using thecombined income risk based credit score and credit bureau scores, fewergood accounts will be targeted for a reduction in credit line decreases.Good accounts can also be granted credit line increases due to theenhanced risk separation and discrimination. Thus, through the presentinvention, the Applicant uniquely addresses the missing component ofaccurately predicting consumers' payment default risk, or credit risk,using future ability to pay using income risk, allowing credit scoringmodels to make better, accurate determination of consumers' credit risk.

DESCRIPTION OF THE DRAWINGS

For the present invention to be clearly understood and readilypracticed, the present invention and its embodiments will be describedwith the following figures, wherein like reference characters designatethe same or similar elements, which figures are incorporated into andconstitute part of the specification, wherein:

FIG. 1 is a flow chart showing the overall process of the invention;

FIG. 2 is a diagram depicting key drivers of consumer credit risk ofwhich the ability to pay is predicting by the invention;

FIG. 3 is a table showing the consumer payment risk matrix;

FIG. 4 is a chart demonstrating the two key drivers of consumers' totalcredit risk for unsecured loans;

FIG. 5 depicts exemplary components of the consumer income risk scoregenerated by the system;

FIG. 6 is a diagram showing the steps involved in computing theconsumer's income risk based credit score by the invention;

FIG. 7 is a diagram showing the steps involved in creating a consumerincome risk scoring model by the invention;

FIG. 8 is a diagram showing the steps involved in creating an incomerisk based ability to pay risk model by the invention;

FIG. 9 is a diagram showing the steps involved in the computation of aconsumer income risk based credit score;

FIG. 10 describes the limitations of traditional Credit Bureau Scoresand shows the components of one of its embodiment, the FICO score;

FIG. 11 describes the key features and limitations of alternative creditscores and lists major alternative scores in existence today;

FIG. 12 is a table describing existing credit scoring models and theirlimitations;

FIGS. 13 and 13A are charts comparing loss curves for Income Risk BasedCredit Score and a traditional credit score;

FIG. 14 is a chart showing odds ratios for Income Risk Based CreditScore;

FIG. 15 is a table showing superior risk segmentation capability ofIncome Risk Based Credit Score over traditional credit score for paymentdefault risk;

FIG. 16 is a chart showing Income Risk Based Credit Score and itsability to predict mortgage insurance claims;

FIG. 17 is a chart illustrating LEHI's ability to track GDP because LEHIeconomic indicator can be used by the invention in computing Income RiskBased Credit Score;

FIG. 18 is a chart showing JSI values for sample ZIP codes which can beused by the invention in computing Income Risk Based Credit Score;

FIG. 19 is a chart which shows that credit risk truly depends onconsumers' future ability to pay and their future willingness to pay;

FIG. 20 is a chart showing LEHI's ability to predict local economichealth;

FIG. 21 is a chart showing Income Risk Based Credit Score (itsembodiment as JSS) and its ability to payment default risk (delinquencyrate);

FIG. 22 is a chart comparing the predictive power of Income Risk BasedCredit Score (its embodiment as JSS) and traditional credit score (itsembodiment as VAN score) for payment default risk (loss rate or badrate);

FIG. 23 is a table comparing the predictive power of Income Risk BasedCredit Score (its embodiment as JSS) and its effectiveness as a creditscore using statistical analysis (KS stats);

FIG. 24 is a chart for Income Risk Based Credit Score (its embodiment asISS) versus traditional response score (its embodiment as RESP SCR) forprospect scoring;

FIG. 25 is a chart comparing Income Risk Based Credit Score (itsembodiment as ISS) and a traditional response score (its embodiment asRESP SCR) for predicting prospect response rates;

FIG. 26 is a chart for Income Risk Based Credit Score (its embodiment asJSS) and traditional credit score (its embodiment as VAN score)comparing payment default risk (loss rate or bad rate) for prescreenedaccounts;

FIG. 27 is a chart for Income Risk Based Credit Score (its embodiment asJSS and ISS) versus traditional response score (its embodiment as RESPand VAN SCR) for prospect scoring;

FIG. 28 shows two tables comparing K-stats for Income Risk Based CreditScore (its embodiment as JSS and ISS) and a traditional response score(its embodiment as RESP and VAN SCR) for prospect scoring;

FIG. 29 is a chart showing the relationship between Income Risk BasedCredit Score (its embodiment as JSS_IN) and consumer delinquencies;

FIG. 30 is a chart comparing effectiveness of Income Risk Based CreditScore (its embodiment as ISS) and traditional credit score (itsembodiment as Vantage) in predicting response rates;

FIG. 31 is a chart comparing effectiveness of Income Risk Based CreditScore (its embodiment as ISS) and a response score in predictingresponse rates;

FIG. 32 is a chart comparing Income Risk Based Credit Score (itsembodiment as ISS) and traditional credit score (its embodiment asVantage) in predicting payment default risk (bad rates);

FIG. 33 shows two charts comparing the effectiveness of Income RiskBased Credit Score (its embodiment as JSS) and traditional credit score(its embodiment as CBS score) in predicting payment default risk;

FIG. 34 shows two charts comparing the effectiveness of Income RiskBased Credit Score (its embodiment as JSS) and traditional credit score(its embodiment as CBS score) in predicting payment default risk;

FIG. 35 shows tables comparing Income Risk Based Credit Score (itsembodiment as JSS) with traditional credit score (its embodiment as CBSscore) for payment default risk;

FIG. 36 is a table comparing Income Risk Based Credit Score (itsembodiment as JSS) with traditional credit score (its embodiment as CBSscore) for payment default risk;

FIG. 37 is a chart comparing Income Risk Based Credit Score (itsembodiment as JSS) versus traditional credit score (its embodiment asCBS score) for payment default risk;

FIG. 38 is a chart comparing Income Risk Based Credit Score (itsembodiment as JSS) versus traditional credit score (its embodiment asCBS score) for payment default risk and ability to identify high riskaccounts (bads);

FIG. 39 is a chart comparing Income Risk Based Credit Score (itsembodiment as JSS) versus traditional credit score (its embodiment asCBS score) for cumulative bad rates;

FIG. 40 shows two charts showing Income Risk Based Credit Score (itsembodiment as JSS) and its ability to predict payment default risk(delinquencies) in 3 months and 6 months from the time of booking theaccounts;

FIG. 41 is a diagram showing Income Risk Based Credit Score (itsembodiment as JSS) and its ability to predict payment default risk(delinquencies) for existing accounts;

FIG. 42 is a chart showing Income Risk Based Credit Score (itsembodiment as JSS) and traditional credit score (its embodiment asRiskscore) and their abilities to predict payment default risk (firstpayment default or FPD default rates);

FIG. 43 is a chart showing Income Risk Based Credit Score (itsembodiment as ISS) and its ability to predict customer conversion rate;and

FIG. 44 is a diagram of the computer implemented system for generatingand providing an Income Risk Based Credit Score.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

It is to be understood that the figures and descriptions of the presentinvention have been simplified to illustrate elements that are relevantfor a clear understanding of the invention, while eliminating, forpurposes of clarity, other elements that may be well known. Those ofordinary skill in the art will recognize that other elements aredesirable and/or required in order to implement the present invention.However, because such elements are well known in the art, and becausethey do not facilitate a better understanding of the present invention,a discussion of such elements is not provided herein. The detaileddescription will be provided herein below with reference to the attacheddrawings.

Generally speaking, the present invention provides systems and methodsfor a novel income-risk based credit scoring system to predictconsumers' credit risk by using their income risk, which is key driverof credit risk.

Consumers are able to pay their debt obligations when they have money(FIG. 3), and consumers typically rely on a steady source of income tobe able to manage their financial obligations. When their source ofincome disappears, reduces, or is adversely impacted, which is usuallybecause of job loss or change, then the consumer's ability to pay isdiminished and their probability of payment defaults increases, whichresults in the fact that they become less creditworthy and presenthigher credit risk to their lenders.

FIG. 3 is a table showing the consumer payment risk matrix which showsthat consumers make a payment if they have both the willingness andability (301) and do not make a payment if they don't have thewillingness or the ability (302 and 304) or have neither the willingnessnor the ability (303).

Until recently, consumers' income risk and their ability to pay riskwere not defined or included in credit scoring models (FIG. 11 and FIG.12), making it impossible to predict a consumer's true total creditrisk. FIG. 11 describes the key features and limitations of alternativecredit scores (1101) and lists major alternative scores in existencetoday (1102). FIG. 12 is a table describing existing credit scoringmodels and their limitations which consist of credit bureau scores(1201), lenders' internal and custom credit scores (1202), andalternative credit scores (1203);

The present invention is the first to develop a computer implementedsystem for quantifying (FIG. 44) consumers' credit risk due to incomeloss by providing a credit scoring system using unemployment riskprobability, and using income risk (FIG. 7), called the income riskbased credit score (FIG. 1 and FIG. 9), of which the Job Security Scoreis a preferred embodiment. The Job Security Score predicts theprobability of an individual defaulting on credit obligations byfactoring in the probability of income loss.

FIG. 44 is a diagram of the computer implemented system for generatingand providing an Income Risk Based Credit Score and its embodiments andit comprises of a database bank (4401) consisting of a consumerdatabase, economic database and a modeling database; a data processingunit (4402) consisting of a modeling server, a communication server, anda database server, a memory storage unit (4403); a consumer scoregenerating computer (4404) which produces the Income Risk Based CreditScore and its embodiments; an administrator workstation computer (4405)which manages the access, read, and write privileges to various usergroups in the system connected through an internal LAN (4408); aninternet and VPN connection (4406); access to client computer (4409) forexchange of consumer data and scores; and access to external databases(4407) for exchange of consumer data and scores.

FIG. 1 is a flow chart showing the overall process of the invention.Consumer income risk (101) is the probability that a consumer will havethe necessary income to pay their debts, i.e. consumer's ability to payrisk (102). Factored into the risk calculations are the consumer'spayment default data (103), which can be comprised of at least thefollowing: credit card default data (106), mortgage default data (107),auto loan default data (108), and other debt default data (109). Block104 depicts the step whereby the invention analyzes and correlatesconsumers' payment default data with their income risk using statisticalmodels predicting consumer credit risk (105). The invention generates aconsumer income risk based credit score (110).

FIG. 7 is a flow chart depicting the steps to be carried out in theconsumer data analysis process in order to generate the novel consumerincome risk scoring model (707). The consumer data (701) is used toplace the consumer in a risk group, wherein consumers with similarattributes are placed in risk groups (703), and is analyzed withhistorical unemployment data (704), historical income disruption data(705), and historical economic data and many economic indicators (702).The invention then analyzes, correlates, and establishes historicalmathematical relationships (706) between consumer's unemployment andincome disruption data and economic data resulting in the consumerincome risk scoring model (707).

FIG. 9 is a diagram showing the steps involved in the computation of aconsumer income risk based credit score (909) which comprises ofdeveloping a consumer income risk scoring model (901) to generate anincome risk score (902), developing a consumer ability to pay scoringmodel (903) to generate an ability to pay score (904), using consumers'historical payment default data for a large number of actual loans(905), establishing mathematical relationships between income risk,ability to pay, and payment default data (906), predicting paymentdefault risk for consumer risk groups, categories and subcategories(907), and developing a consumer income risk based credit scoring model(908).

Two fundamental concepts of consumer credit risk are the “willingness”to make repayment on a loan and the “ability” to repay (FIG. 2 and FIG.4). While willingness can be judged by past credit behavior, the abilityto pay is dependent upon external factors beyond the consumers' control(e.g. loss of income, medical problems, divorce, unemployment, etcetera)of which the probability of continuance of future income, or incomerisk, due to unemployment is the one of the biggest factors.

FIG. 2 is a diagram displays the three main categories of the creditrisk. They are collateral (201), willingness to repay (202) and abilityto repay (203), only two of which (collateral and willingness) have beenpreviously incorporated in prior art. As credit cards are not secureloans, the willingness to pay alone will not tell the lender all thepertinent information about a consumer. A reliable indicator of futureeconomic ability is necessary in order to determine the true credit riskof the consumer. Through the invention, the prediction of consumers'ability to pay makes lenders' decisions more accurate.

FIG. 4 is a chart demonstrating the two key drivers of consumers' totalcredit risk (403) for unsecured loans comprising of willingness (401)and ability (402).

Since credit risk is truly a measure of payment default probability infuture, future willingness and future ability to pay are the realdrivers of consumer credit risk than past willingness and past ability(FIG. 19). Thus, there is an unmet need in the marketplace toincorporate the ability to pay risk in credit scoring models in order todecrease credit lending risks. Recognizing this unmet need, the presentinvention provides a highly predictive model that predicts the abilityto pay component. This results in a new consumer risk score, the incomerisk based credit score, of which the Job Security Score is a preferredembodiment, which predicts consumers' payment default risk usingconsumers' income risk as a factor.

FIG. 19 is a chart which shows that total credit risk (1903) trulydepends on consumers' future ability to pay (1902) and their futurewillingness to pay (1901) rather than on their past willingness and pastability, and this is a critical distinction that needs to be understoodbecause credit risk by definition is an assessment of future paymentdefault probability so what really matters is the future willingness andability of the consumer to make payments.

By monitoring economic conditions, and establishing relationshipsbetween economic activity, consumers' income, consumers' financialbehavior, consumers' ability to pay, consumers' ability and willingnessto buy, and consumers' wellbeing (FIG. 8 and FIG. 9), the presentinvention has been able to quantify consumers' credit risk that isdependent on their income risk, and this approach is a novel one whichcredit bureaus have yet to conceive.

FIG. 8 shows an exemplary capacity to generate an ability to pay riskmodel. Consumer profile data (801), consumer unemployment histories(802), and income disruption histories (803) were the first elementsused in the process to predict consumers' income disruption risk. Then,consumers financial stress data is correlated with income disruptionrisk (804) and mathematical relationships are established betweeneconomic conditions and consumer financial stress (805). Saidcombination creates the statistical model predicting consumers'financial stress for present and forecasted economic conditions (806).Said historical and forecasted economic data (807) integrated withconsumer profile data results in the income risk based ability to payrisk model utilized by the invention. Said model can then be used bylenders in making consumer credit based decisions.

Jobs and unemployment incidents are affected by the economy. During weakcycles, demand is down, whereas during strong cycles, demand is up. Froman individual perspective, the job loss risk is a function of the supplyand demand in the labor market, which is driven by economic conditions(FIG. 17, FIG. 18, and FIG. 20).

FIG. 17 is a chart showing LEHI's (Local Economic Health Indicator)ability to track GDP as LEHI, which can be optionally used in computingIncome Risk Based Credit Score. Such current economic information beingupdated monthly by the invention produces the most current statistics onwhich lenders can base their decisions. The lower the LEHI of an area,the higher the payment default risk is for that area.

FIG. 18 is a chart showing how JSI (Job Security index) varies by ZIPcodes. This is valuable information which can be used in computingIncome Risk Based Credit Score.

FIG. 20 is a chart showing LEHI's ability to predict local economichealth. This chart demonstrates that national economic health andregional economic health, such as for Stockton, California's MSA can bequite different and economic variations such as these can beincorporated into the present invention for predicting consumer incomerisk.

Credit bureau models do not adapt or reflect the changing economy. Adelay of around 12 months or more is common in the credit bureau modelsand the changes are only considered as a derivative effect resultingfrom consumers' altered credit behavior. Yet, with a continually growingamount of consumer data being correlated into the system, Applicantpredictions become more accurate, giving the lender greater ability toanalyze consumer risk (FIG. 5 and FIG. 6). Thus, models that incorporatethe Job Security Score will adapt quickly to reflect the latest economicconditions and forecasts, providing a more accurate and “true” creditrisk prediction.

FIG. 5 depicts exemplary elements of the consumer income risk score (orincome disruption risk) (501) analyzed by the invention. The consumerincome risk score is the probability of the consumer's future employment(and therefore income) calculated twelve (12) months in advance andreduced into a score. Said score is a measure of the likelihood theconsumer will be able to repay a debt and comes in the form of a numberbetween −1000 to +1000, or any other numerical or non-numerical range orscale. Said score is independently assessed to each individual consumerand it varies based on consumer's income risk and credit risk. Such anaccurate indicator of ability to pay for each consumer is essential is acore aspect of the current invention. The following elements areincorporated into the income risk score: The consumer's unemploymentrisk (502), consumer's income loss risk (503), consumer's incomereduction risk (504), and consumer's income variability and volatilityrisk (505). The consumer's probability of a becoming unemployed andexperiencing a reduction in their income is then weighted andincorporated into the final consumer income risk score.

FIG. 6 is a flow chart showing the advantages of the current invention.The invention takes a selected consumer (601) and collects consumerprofile data (602) (further described in FIG. 8) on said customer. Thisinformation is then used by the method to compute the consumer's incomedisruption risk (603) using outputs from method described in FIG. 5.Using (601), (602), and (603), the invention computes the consumer'sincome risk score (604). Said credit score can be used for the followingpurposes: To predict consumer's payment default risk and credit risk forloans (605) through the consumer's income risk based credit score (606);and to estimate consumer's response rate for credit card offers andother marketing offers (607) through the consumer's income risk basedprospect score (608). The current invention allows lenders the muchneeded ability to pay insights for their consumers and potentialconsumers. No existing credit scoring model incorporates a prediction ofconsumer's income risk in predicting payments defaults and credit risk.

Income loss due to unemployment incidents are related to several factorsthat can be categorized into three groups: macroeconomics (e.g. GDP,money supply, M1, M2, energy prices, etc.), macro-demographics (i.e.factors that pertain to a population of individuals such as occupationindustry, occupation type, zip code, etc.) and micro-demographics (e.g.income, years at residence, highest level of education, etc.).

The present invention provides a unique risk scoring model ofunemployment incidents using vast amounts of economic data and actualconsumer data (FIG. 5 and FIG. 6). With a proprietary data collectionoriginating 6 years ago, the present invention is able to collect dataon consumers' historical unemployment incidents, prevailing economicconditions and historical trends, consumers' post unemployment financialstress situation, unemployment severity and payment defaults data. Usingsophisticated data-mining techniques and statistical algorithms, thepredicative analytical model finds patterns and relationships betweeneconomic indicators and an individual's profile to predict the person'slikelihood of income loss due to unemployment within the next 12 months(FIG. 8 and FIG. 9). The Job Security Score has been developed usingthousands of actual individual profiles, hundreds of macroeconomicvariables covering decades of local, regional, and nation economictrends, and the credit behavior of millions of actual borrowers (FIG.7). Analysis proved that the unemployment risk scores used by thepresent invention are over 85% accurate in predicting unemployment riskstwelve months in advance and are better predictors of consumers' paymentdefault risk.

Previously, the consumer's ability to pay risk was not defined orincluded in credit scoring models and that they had many limitations(FIG. 10) making it impossible for them to predict consumers' incomerisk and therefore they were unable to predict consumers' true creditrisk. Inclusion of income risk by lenders and credit card issuers intheir credit matrix will significantly improve the accuracy andeffectiveness of their credit risk prediction capabilities. FIG. 10describes the limitations of traditional Credit Bureau Scores (1001) andshows the breakdown of one of its embodiment, the FICO score (1002).

In one preferred embodiment, the invention provides an application ofstatistical algorithms to find patterns and relationships betweeneconomic indicators to predict the likelihood of future events with highlevels of accuracy. The invention quantifies the inherent income risk inthe form of an income risk score. This information can then be used bythe income risk based credit score, of which the Job Security Score is apreferred embodiment, to predict consumer behavior, delinquency,charge-off risk, spending trends, likelihood of on-time payments,effectiveness of products or services, and virtually any other factorthat can be statistically analyzed and related to consumer's income inmaking superior assessment of credit risk over traditional credit scores(FIGS. 13, 13A, 15, 16, 21, 22, 23, 26, 27, 29, 30, 31, 32, 33, 34, 35,36, 37, 38, 39, 40, 41, and 42).

FIG. 14 is a chart shows odds ratios for payment default risk for theIncome Risk Based Credit Score (its embodiment as JSS). As shown, theJSS is able to predict and rank order payment default risk very well.

FIG. 15 is a table which shows how the Income Risk Based Credit Score(its embodiment as JSS) is able to identify more good accounts withoutincreasing a lender's loss rate. The JSS allows the lender to decreasetheir existing credit score cutoff (its embodiment as Custom Score) andyet not increase their loss risk because of JSS's ability to segmentgood accounts (in blue) and bad accounts (in red) that was not possiblebefore.

FIG. 16 is a chart showing Income Risk Based Credit Score (itsembodiment as JSS) and its ability to predict mortgage insurance claims(Payment defaults). In using the consumer score in connection withmortgages, lenders can acquire a more informed rational for a mortgagedecision. The higher the consumer score, the more likely they are torepay their debts.

FIG. 21 is a chart showing Income Risk Based Credit Score (itsembodiment as JSS) and its ability to predict payment default risk(delinquency rate). As can be seen, the JSS is able to predict and rankorder delinquencies very well.

FIG. 22 is a chart showing loss curves for Income Risk Based CreditScore (its embodiment as JSS), a credit bureau score (its embodiment asVAN score), and for a combined JSS+VAN score. As can be seen, the JSS isable to increase good accounts by 15% and decrease bad accounts by 11%clearly demonstrating that JSS offers new credit risk insights.

FIG. 23 is a table showing KS-stats (a higher KS indicates betterpredictive power) for Income Risk Based Credit Score (its embodiment asJSS), a credit bureau score (its embodiment as VAN score), and for acombined JSS+VAN score. As can be seen, the JSS is able to increaseKS-stats by 50% over existing VAN score.

FIG. 26 is a chart showing loss curves for Income Risk Based CreditScore (its embodiment as JSS), and a credit score (its embodiment as VANscore) and for a combined JSS+VAN score. As can be seen, the JSS is ableto increase good accounts by 25% and decrease loss rates by 5% clearlydemonstrating that JSS offers new credit risk insights and has superiorrisk separation capabilities.

FIG. 27 is a chart showing loss curves for Income Risk Based CreditScore (its embodiment as JSS and ISS), a response score (its embodimentas RESP score), a credit score (its embodiment as VAN score), and for acombined ISS+RESP score As can be seen, the ISS can be used to approvegood accounts that were declined by the use of existing credit scores.

FIGS. 29 to 42 show various comparisons between Income Risk Based CreditScore (its embodiment as JSS and ISS), a response score (its embodimentas RESP score), a credit score (its embodiment as FICO and VAN score)and demonstrate how the Income Risk Based Credit Score is able to addnew consumer insights in credit scoring and prospect scoring.

The present invention predicts income-loss risk by measuring how economyis changing and how it impacts consumers' income and income risk toforecasts future paying capacity—or paying ability—of consumers. Thisoffering of new and in-depth comprehension into future consumer creditbehavior has not yet been captured by any credit bureau scores or othercredit scores. In capturing economic impact on an individual consumer'sability to pay, the present invention utilizes elements not previouslyavailable in the field. Further, the Job Security Score can be used asthe primary and sole credit score to predict credit risk or it can beused in conjunction with current scoring methods (FIGS. 13 and 13A). Bycreating a way to predict a separate aspect of consumer risk originatingfrom their income risk, the present invention greatly enhances theaccuracy of consumer credit risk assessment. FIGS. 13 and 13A are chartscomparing Income Risk Based Credit Score (its embodiment as Job SecurityScore or JSS) against a traditional credit score (its embodiment asFICO) for payment default risk and they show that JSS improves riskprediction. The Job Security Score (or JSS) is an embodiment of theinvention and measures the ability to pay, and credit risk, of anindividual. This chart shows loss curves for JSS and FICO and itdemonstrates the superior ability of the JSS over FICO in predicting andsegmenting good and bad accounts.

The income risk based credit score, of which the Job Security Score is apreferred embodiment, is also a prospect score and capable of scoringany consumer in the marketplace. All existing scoring models and methodsare reliant on credit data and/or payment data, but none include incomerisk except present invention. In implementing a scoring model that isaccurate in its credit risk assessments, lenders can improve riskassessment by an average of 11%. Testing shows an average of 9% lift forthick file credit card portfolios and 30% lift for thin-file and no-hitcredit card portfolios. Considering the approximately 70 millionconsumers who are not scored have a much high delinquency rate than theaverage consumer, the income risk based credit score, of which the JobSecurity Score is a preferred embodiment, will greatly benefit lenders.

In one preferred embodiment, the income risk based credit scoring modelgenerates a Job Security Index (JSI) that measures the job conditionsfor a MSA/Zip location. By correlating with consumer spending, creditcharge-offs, and delinquencies, the JSI is a useful prospective scorewhen evaluating how consumer behavior is likely to change with fluxes inthe economy. Because the Job Security Score is updated on a monthlybasis, the JSI is able to reflect current conditions (FIG. 18).Additionally, the JSI can be applied to any consumer lists in order toidentify better prospects.

In another preferred embodiment, the present invention will improveacquisitions because of better-informed targeting and segmentation ofprospects which the invention provides (FIG. 24, FIG. 25, FIG. 28, andFIG. 43).

FIG. 24 is a chart for Income Risk Based Credit Score (its embodiment asIncome Stability Score, or ISS in short) versus traditional responsescore (its embodiment as RESP SCR) for prospect scoring.

FIG. 25 is a chart showing response rate curves for Income Risk BasedCredit Score (its embodiment as Income Stability Score, or ISS) and aresponse score (its embodiment as RESP score). As can be seen, the ISSis able to increase response rate by 4%.

FIG. 28 is a table showing KS-stats for Income Risk Based Credit Score(its embodiment as JSS and ISS), a response score (its embodiment asRESP score), a credit score (its embodiment as VAN score), and for acombination of above scores. As can be seen, the ISS is able to increaseKS-stats by 49% and 22% over existing RESP scores.

FIG. 43 is chart shows the income risk based credit score (itsembodiment as ISS) and its ability to predict customer conversion rate.The ISS can be used for alternative purposes, such as identifyingcustomer conversation rate as displayed.

When lenders use income risk based credit score, pricing, loan amounts,and product offering decisions will be more effective and profitable dueto new credit risk insights not available through conventional creditbureau scoring. Further, delinquencies and charge-offs will decrease dueto early identification of high risk accounts. Businesses can now marketto more promising prospects and approve more applicants based on the“true” overall credit risk, rather than basing their decisions on anoutdated view of credit risk offered by credit bureau scores alone.Prospect identification processes will be more effective as mostidentified prospects will match the ideal customer profile requirements.The offer response rate will improve as well because of the greaterprecision in targeting. The approval decision process is enhanced due tobetter separation of applicant's credit risk. By gaining insights intoability to pay risk using Job Security Score, businesses can now get acomplete picture of consumer's credit risk thereby improving theirportfolio's size and quality while giving them a significant competitiveadvantage.

It is noted that there are an infinite number of ways to createhomogenous classes of people with similar risks for the millions ofpeople nationwide. Because there never has been a personal unemploymentrisk score and a consumer income risk score, there is no actuarial dataavailable by any established risk classes related to unemployment rates.Therefore, the present invention also presents a method and model tosegment the labor force into homogenous unemployment risk classes andestablishes empirical relationships between historical unemploymentrates and risk classes and income risk.

Another aspect of the present invention provides account managementstrategies using the Job Security Score. By monitoring the Job SecurityScore of accounts on a constant basis (scores are updated every month)lenders can identify a high risk account before the account actuallybecomes a high risk/bad account—unlike credit bureau scores whichdeliver the news after the account has negative items on file. Thus, thenew predictive power enables lenders the time and insights to takestrategic initiatives to manage and mitigate the risk before it is toolate.

In an additional embodiment, the present invention provides forbetter-informed credit line decisions. Both credit line decreasedecisions and balance build/balance transfer offer decisions are mosteffective and profitable when they are based on the latest, mostcomplete, and most accurate assessment of consumer's risk. Since JobSecurities Scores change every month, even for consumers whose profilesremain unchanged, they reflect the latest economic conditions that mayimpact jobs and income prospects. Lenders will always have the latestand most accurate risk assessment possible, allowing them to deploy moreprofitable credit line increase and decrease strategies.

When the Job Security Score is combined with credit bureau scores, orother internal risk scores, it redistributes the population in such away that lenders can lower their cut-off credit bureau scores allowingfor more approvals, without lowering the risk threshold (FIGS. 13, 13A,14, and 15). In fact, at the same time, the risk profile of theportfolio screened and managed using a combined score (e.g. Job SecurityScore+FICO) decreases because risk assessment is more accurate, leadingto an increase in portfolio quality and a decrease in losses (FIGS. 13,13A, 14, 15, 16, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,35, 36, 37, 38, 39, 40, 41, 42, and 43).

The present invention also preferably provides improved marketingcapabilities for businesses. With the advanced prospect scoring,marketing strategies can be more focused and target the idealpopulations. In addition, the present invention enhances anyprescreening of an individual and can be used to strengthen predictions(FIGS. 24, 25, 28 and 43). This allows for the early identification ofhigh risk individuals, narrowing the delinquency probabilities andmaking marketing more sophisticated.

The present invention also preferably provides lenders with thefollowing benefits: a more accurate picture of consumer's credit risk,in both good and bad economic times; a proactive and leading indicatorof credit risk (unlike credit bureau scores, which are reactive andlagging); and improved segmenting and differentiation due to bettercredit risk prediction capabilities (FIGS. 13, 13A, 14, 15, 16, 21, 22,23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42, and 43).

As described above, the present invention provides methods forimplementing an unique indicator of consumer credit risk stemming fromthe ability to pay risk associated with each and every individualapplying for credit. Its applications are in all those areas thatinvolve credit risk assessment or predicting credit dependent behavior.

It is also to be understood that this invention is not limited to usingthe data, records, data elements, variables and field structuresdescribed herein, and other data elements, data, and physical structureswill be equivalent for the purposes of this invention. The invention hasbeen described with reference to a preferred embodiment, along withseveral possible variations; however, it will be appreciated that aperson of ordinary skill in the art can effect further variations andmodifications without departing from the spirit and the scope of theinvention.

Nothing in the above description is meant to limit the present inventionto any specific materials, geometry, or orientation of elements. Manypart/orientation substitutions are contemplated within the scope of thepresent invention and will be apparent to those skilled in the art. Theembodiments described herein were presented by way of example only andshould not be used to limit the scope of the invention.

Although the invention has been described in terms of particularembodiments in an application, one of ordinary skill in the art, inlight of the teachings herein, can generate additional embodiments andmodifications without departing from the spirit of, or exceeding thescope of, the claimed invention. Accordingly, it is understood that thedrawings and the descriptions herein are proffered only to facilitatecomprehension of the invention and should not be construed to limit thescope thereof.

1. A computer-implemented method to predict a consumer's credit risk;wherein said credit risk is the probability of consumer defaulting ontheir payment obligations including credit card debt, personal loans,automotive loans, student loans, mortgage loans, and other types ofconsumer loans; wherein the said credit risk also means the consumer'sability to pay; wherein the said credit risk also means the consumer'scapacity to pay; wherein the said income risk is predicted usingconsumer data; wherein the said income risk is predicted usingconsumer's unemployment risk; wherein the said income risk is derivedfrom consumer's unemployment risk; wherein the said income risk isderived from consumer's income loss risk; wherein the said income riskis derived from consumer's income reduction risk; wherein the saidincome risk is derived from consumer's probability of continuance ofincome; wherein the said income risk is assigned a numerical orqualitative value; wherein the said income risk is correlated withconsumer's credit risk, payment default risk, payment behavior, andability to pay; wherein the said income risk is based on economy'simpact on consumer's income; wherein the said income risk is based oncorrelations between consumer's' personal data and economic conditionsdata including unemployment rates, job growth, wages, inflation, trade,GDP, home prices, construction activity, manufacturing activity, retailsales, and others; wherein the said income risk is transformed into anincome risk score; wherein the income risk score is used to predictconsumer's response behavior, purchasing propensity, and ability to pay;wherein the said income risk is transformed into an income risk basedcredit score to predict consumer's credit risk and payment default risk;wherein the said income risk based credit score is derived from a riskforecasting computer; wherein the risk forecasting computer consists ofa microprocessor CPU, memory, databases, software programs, analyticaland statistical programs, input and output devices, and networkingcapability; and wherein the income risk based credit score is anempirically derived, demonstrably and statistically sound credit scorepredicting consumer's credit risk that is based on their future incomeand future ability to pay; and implementing said data into consumerscoring and scoring systems.
 2. The method of claim 1, wherein saidmethod for determining income risk based credit score further comprisesof the steps: generating by the computer, an unemployment riskprobability for an individual's personal data including age, education,demographic data and employment history, and by using historical andprojected unemployment and hiring trends, historical and projectedmacroeconomic and microeconomic data; generating by the computer, aprobability of an income loss for an individual using unemployment risk;generating by the computer income reduction risk for an individual usingunemployment risk; generating by the computer the probability ofcontinuance of income for an individual using unemployment risk;correlating by the computer, unemployment risk probabilities and incomerisk for individuals in a selected geography, or for individuals in astatistically valid sample comprising hundreds, thousands, or millionsof individuals, with their historical payment defaults, creditdelinquencies, charge-offs, and bankruptcies; correlating by thecomputer, consumers' unemployment risk probabilities and income riskwith consumers' response rates for marketing offers and theirprofitability metrics; correlating by the computer, consumers'unemployment risk probabilities and income risk with microeconomic andmacroeconomic factors; correlating by the computer, consumers'unemployment risk probabilities and income risk with consumers' creditdefault data, credit histories, and payment histories; generating by thecomputer a consumer credit risk model that produces a consumer incomerisk based credit score by finding mathematical relationships betweenconsumers' unemployment risk, income risk and payment default data;generating by the computer a consumer ability to pay prediction modelthat predicts the likelihood of a consumer being able to buy and repay;and processing, storing, transmitting, and rendering using the computerand a computer network, a novel consumer unemployment based credit scoreand a credit scoring system allowing lenders and businesses to scoretheir prospects, applicants, existing accounts and delinquent accounts;existing portfolios and portfolio segments; and new portfolios andportfolio segment; to gain new predictive insights into consumer creditrisk at the individual consumer level and at the portfolio level.
 3. Themethod of claim 1, wherein said income risk based credit score isgenerated based on data selected from the group consisting of, but notlimited to: individuals' personal profile data; individuals' incomedata; individuals' data attributes with risk factors and weighted reasoncodes; national, regional, and local employment and unemployment data;national, regional, and local macroeconomic and microeconomic data;consumers' response rates for marketing offers; consumers' purchasingbehavior and trends; and consumers' payment default data, credit defaultdata, delinquencies data, and bankruptcies data.
 4. The method of claim2, wherein said individual personal data is selected from the groupconsisting of, but not limited to, education, age, job status, jobindustry, job type, job tenure, salary, employment and unemploymenthistory, geographical location, income characteristics, and creditcharacteristics.
 5. The method of claim 2, wherein said nationalemployment, unemployment, and economic data is selected from the groupconsisting of, but not limited to, historical national, regional, andlocal employment and unemployment data, involuntary unemployment data,mass layoffs data, hiring and firing trends, existing and new jobpostings, unemployed population, underemployed population, discouragedworkers population, wage rates, distribution of jobs in industries andoccupations, government unemployment insurance claims, governmentunemployment insurance claim acceptance rates, government unemploymentinsurance benefit payment rates and amounts, duration of governmentunemployment insurance claims, federal and state unemployment insurancefund data, and government insurance program policies and guidelines, andnon-government data.
 6. The method of claim 2, wherein said national,regional, and local macroeconomic and microeconomic data is selectedfrom the group consisting of historical and projected economicindicators including but not limited to: gross domestic product, salesfor retail and food services, durable goods, construction activity,manufacturers' shipments, inventories, and orders; manufacturing andtrade, inventories and sales; monthly wholesale trade, existing homesales, auto sales, new residential construction, new residential sales,construction permits, personal income and outlays. U.S. internationaltrade in goods and services; U.S. international transactions; tradedeficit, consumer confidence, disposable income, and inflation.
 7. Themethod of claim 1, wherein the step of computing a consumer's incomerisk and income risk based credit score further comprises the steps of:segmenting a national workforce population into homogenous riskcategories, with each risk category comprising a plurality of homogenoussub risk subcategories; segmenting dependent, unemployed and non-workingindividuals into risk categories and sub categories; assigning a riskfactor weight to each of the risk categories and sub risk subcategories;predicting an unemployment rate for a finite duration of 1 to 10 years,or any other time frame, for each risk category and sub category;predicting an income loss probability for a finite duration of 1 to 10years, or any other time frame, for each risk category and sub category;transforming the said unemployment rate predictions and income loss riskpredictions, or any mathematical combinations of these, into amathematical score on a scale of zero to one thousand or any othersimilar scale, which may be developed using linear or non-linearmathematical equations; transforming the said unemployment rate scoreand income loss risk score into an income risk based credit score bycorrelating them with individuals' ability to pay and credit data;predicting an ability to pay risk for a finite duration of 1 to 10years, or any other time frame, for each risk category and sub categoryand converting it into an ability to pay score; predicting creditdefault risk for a finite duration of 1 to 10 years, or any other timeframe, for each risk category and sub category and converting it into anincome risk based credit score; and providing a quantitative andqualitative explanation and narrative of the contributing risk factors,relative ranking of a said income risk based credit score's by comparingit with other scores and score groups including, but not limited to,national and regional risk scores, industry and sub-industry scores,education scores, and scores grouped based on economic, credit andpayment behavior, and demographic similarities and other commonattributes.
 8. The method of claim 7, wherein said unemployment riskcategories are selected from the group consisting of education,industry, age, gender, occupation, state, region, income, workexperience, training level, work performance, job change frequency,industry change frequency, historical unemployment data, unemploymentseverity, job necessity, debt-to-income ratio, expenses-to-income ratio,and job confidence.
 9. The method of claim 7, wherein said forecastedunemployment rates are generated based on a mechanism selected from thegroup consisting of national, regional, and local unemployment rates,layoff data, job hiring trends, consumer price index, producer priceindex, interest rates, trade balance, housing starts, industrialproduction, currency exchange rates, retail sales, personal income andcredit, consumer expenditure, industry capacity utilization, governmentspending, capital spending, consumer confidence and non-government data.10. The method of claim 1, wherein the utilizing the income risk basedcredit score includes quantifying by the computer the credit risk of theindividual to predict credit default risk, payment and repaymentbehavior, payment default risk, delinquency risk, charge-off risk,bankruptcy risk, likely spending trends, likelihood of on-time payments,and the effectiveness of products or services to provide a more accurateassessment of an individual consumer's credit risk.
 11. The method ofclaim 1, wherein the computation of income risk based credit scorefurther comprises of the steps of: determining by computer theprobability of unemployment risk of an individual consumer or aborrower; determining by computer the probability of income loss risk ofan individual consumer or a borrower; correlating by computer the incomeloss risk of an individual consumer or a borrower with credit defaultdata and payment default data; analyzing by computer the correlation andstatistical relationships between actual unemployment data, income lossdata, and credit default data for hundreds, thousands or millions ofindividuals; correlating and comparing by computer the actual andprojected income loss risk with actual and projected credit risk andpayment default data for thousands or millions of individuals;correlating by computer the income loss risk with future ability to payfor an individual consumer or a borrower; establishing statisticalrelationships and mathematical equations between ability to pay andcredit risk through retro tests and back tests by working with relevantbanks, financial institutions, credit unions, credit bureaus, datawarehousing companies, and other companies which have individual leveldata; transforming by computer the income loss risk and ability to payfactors into a probability of payment default for an individual consumeror a borrower; transforming by computer the probability of paymentdefault into an income risk based credit score consisting of athree-digit or four-digit number, or any mathematical score or grade, orany other similar embodiment that quantifies and reflects differentdefault probabilities into discernible patterns; developing by computera statistically valid and empirically sound credit scoring model thatpredicts an individual's credit risk by using the income risk basedcredit score; validating the income risk based credit score's predictivepower and risk separation capability by testing the score using actualconsumer data; assigning the said income risk based credit scoreappropriate marketing and product names, such as the Job Security Score,Income Risk Score, Income View Score, Income Credit Score, IncomeProspect Score, and Income Continuance Score; correlating by computerthe predictive power of the income risk based credit score withperformance characteristics of a specific loan portfolio, or with manydifferent loan portfolios; establishing odds ratios and loss curves forincome risk based credit score for different loan portfolio typesincluding marketing, acquisitions, account management and collections;developing income risk based credit score usage strategies for amultitude of lending and lending related decisions including creditapproval decisions, credit line decisions, up sell and cross selldecisions, rewards strategies, account treatment strategies, collectionstrategies, delinquency management strategies, charge off and lossmitigation strategies, portfolio sale and purchase strategies, portfolioasset valuation strategies, portfolio securitization strategies, andforecasting losses and revenues; and developing and rendering incomerisk based credit score for use by marketers, lenders and companies foruse as a prospect score, primary credit score, and as a secondary creditscore to be used in conjunction with other types of credit scores andalternative credit scores.
 12. The method of claim 1, wherein the saidincome risk based credit score can be is modified or customized by:combining by the computer, the income risk based credit score withcredit bureau scores and consumer risk scores using equal on ornon-equal weights for each; customizing by the computer the income riskbased credit score, credit bureau scores and consumer risk, and aselected portfolio's credit performance data; and producing at thecomputer, a more comprehensive consumer credit risk score a and creditscoring system for a specific pool of accounts, portfolio, or lender,allowing business to obtain a more predictive and accurate assessment oftheir consumers' credit risk.
 13. The method of claim 1, furthercomprising of the step of updating the income risk based credit scorefrequently and periodically such as monthly, quarterly or yearly, andany other suitable frequency, to capture the latest and best possiblemeasure of economy's impact on consumers' income risk and credit risk.14. The method of claim 11, further comprising the step of combining theincome risk based credit score with any of the existing credit bureauscores or risk scores in order to increase approvals by redistributingthe population through segmentation and differentiation based onadvanced credit risk prediction capabilities.
 15. The method of claim 1,wherein in one embodiment of income risk based credit score, the JobSecurity Score, predicts a payment default risk for an individual for afinite future, such as a period of up to thirty six months or any otherfinite period from the time of scoring, by using a set of inputvariables selected from the group consisting of but not limited to wherethe personal data further comprises age, personal income, total debt,debt ratio (debt/available debt), number of times delinquent in last twoyears, savings account information (if one exist), residency (city,state, and zip code), years at current residence, own/rent status, localyearly income, highest level of education, educationdiscipline/concentration, year attained, educational institution, yearsof full time work experience, current employer, length of time withpresent employer, self-employment (if any), part-time/full-time status,work city, state and zip code, job occupation area, employer's industry(name, SIC code), and total employees at place of work.
 16. The methodof claim 1, wherein in one embodiment of income risk based credit score,the Job Security Score, becomes an FCRA compliant credit scorepredicting consumer credit risk for an individual for a finite futureusing only FCRA compliant input variables.
 17. The method of claim 1,wherein in one embodiment of income risk based credit score, the IncomeStability Score, predicts ability to pay and buy for an individual foruse as a prescreening score and as a prospect score for identifyingprospects and for predicting response rates for marketing purposes. 18.The method of claim 1, wherein the income risk based credit score isgenerated by using a system consisting of a computer-readable mediumhaving computer-executable instructions for performing a methodcomprising of: creating databases to store national, regional and localemployment and unemployment data, economic data, and consumers' personaldata; adding and refreshing new data into said databases; updating saiddatabases with derivative data and predicted data using mathematicalprocesses; and forecasting unemployment risk and income risk forhomogeneous risk groups in order to measure and predict an individual'sincome risk based credit score and Job Security Score.
 19. The method ofclaim 18, wherein the process of computing, generating and renderingincome risk based credit scores further comprises of the steps includingestablishing a computer-based method and system based on scoring andprocessing elements selected from the group consisting of mathematicalequations, statistical relationships, algorithms, computer software,computing systems, mathematical models, advanced programs, electronicdatabases, analytical tools, statistical software, computer networks,data transfer protocols, internet and intranet, web based userinterface, VPN, EDI, security protocols, dynamic handshake methods,batch scoring, real time scoring, partner network, partner's computersand servers, and ERM systems and processes.
 20. A computer-implementedmethod to compute a short term and a long term employment basedcreditworthiness score and index utilizing an individual consumer'spersonal unemployment probability, income risk, and probability ofcontinuance of income on a mechanism selected from the group consistingof unemployment risk scores, projected unemployment rates for short termand long term, current income, expected income growth for short term andlong term, expected duration of employment for short term and long term,current and expected education level, expected job changes, current andfuture cost of living projections, job change history, and incomehistory.
 21. (canceled)